Fog results in degradation of contrast and color saturation in the images shot outdoor. Therefore

the visibility of objects will decline

and details will be difficult to recognize. Thus

robust defogging techniques are valuable in the industrial fields driven by outdoor images or videos. Currently

the mainstream defogging methods are based on the foggy image degradation model

and two main problems that come from the estimation of atmospheric lights and transmissions remain. First

sky regions do not comply with the dark channel prior. The atmospheric light

which suffers from the interference of sunlight in the images with large sky area

is overestimated

resulting in gloomy sky regions in the fog-free images. Second

halo effects require being eliminated through a necessary transmission refinement process. However

the existing refinement methods will result in unreasonable transmissions with texture-like fluctuations inside the same planar objects

causing the inconsistency of variation trends between the transmission map and the depth information. Furthermore

these transmission fluctuations exert a negative influence on the contrast enhancement in the non-sky regions

which conform to the dark channel prior. To address the abovementioned problems

we combine sky detection with texture smoothing and propose a new image defogging algorithm. First

we design an adaptive atmospheric light estimation strategy based on sky detection

which can avoid gloomy restoration results of the sky regions

to address the overestimation of atmospheric lights in the images with sky regions. Initially

the foggy images are classified according to whether these images encompass sky regions. For the foggy images with sky

the pixels within the sky regions are sorted by their luminance values. The atmospheric light values are estimated according to the sky pixels with low luminance values. The problem of overestimated atmospheric light can be overcome through this strategy

resulting in bright and clean sky regions without color distortion phenomenon. For the foggy images without sky

a SVM-based atmospheric light validation strategy is adopted to avoid the interference of highlight objects and estimate reliable atmospheric light values. Second

we suggest a precise transmission calculation strategy based on patch shift and guided filter to solve the problem of insufficient contrast enhancement for the non-sky regions. At the first step

the input images are preprocessed by texture smoothing to simultaneously suppress the unnecessary texture details inside the same planar objects and maintain the necessary boundary information among the different objects. Therefore

the smoothed input images can maintain color consistency inside the same planar objects. Next

rough transmissions are estimated through the patch shift mechanism to restrain the halo effects

which are then refined with the smoothed input images by the guided filter. Thus

the refined transmissions are consistent with the trend of the depth variation

which is beneficial in promoting the contrast and color saturation of the fog-free images. Third

the fog removal results are post-processed with the original input images by the joint bilateral filter to prevent the negative impact from the noise inside the area with low luminance. For the foggy images with sky regions

relatively lower atmospheric lights can be obtained by our adaptive atmospheric light estimation strategy

and the problem of overestimating atmospheric lights is conquered. Therefore

the restoration results of the sky regions that do not comply with the dark channel prior are bright and clean

without color distortion phenomena. For the foggy images without sky regions

the selected locations of the atmospheric lights will avoid the highlight objects and lie in the regions with highest fog density by using the SVM-based atmospheric light validation strategy. Second

the texture details in the preprocessed images are suppressed fully through texture smoothing

and the pixel color information within the planar objects maintain homogeneous and continuous. The refined transmissions with our precise transmission calculation strategy hold high consistency with the depth variation under the guidance of the texture-smoothed images

and the restored non-sky regions that conform to the dark channel prior present high contrast and color saturation. Moreover

the noise in the fog-free images is reduced after the joint bilateral filtering process. For the gloomy sky regions derived from the exceptions to the dark channel prior

reasonable atmospheric lights are concerned with our method

which is different with the previous strategies that split the sky regions and process them alone. Experimental results show that our estimated atmospheric lights have relatively lower values

and the sky restoration results are bright and clean. For the insufficient contrast-enhanced non-sky regions because of texture details in the original input images

the guidance of transmission refinement is preprocessed through texture smoothing

which is different from the direct transmission refinement strategies used in the previous methods. Moreover

our estimated transmissions are consistent with varying depth information

which contribute to the contrast and color saturation enhancement. Compared with the existing image defogging methods

our fog removal results have higher contrast and color saturation with natural sky regions

which are qualified in outdoor application fields

such as video surveillance

traffic regulation

and object detection. However

some excessive color saturation enhancement will lead to local color distortion phenomena

thus a reasonable and practical control strategy for the color saturation enhancement is expected as our next research goal.